Understanding preferences and similarities from user-authored text: applications to search and recommendations

Abstract

OF THE DISSERTATION Understanding Preferences and Similarities from User-Authored Text: Applications to Search and Recommendations by Gayatree Ganu Dissertation Director: Amelie Marian Users rely increasingly on online reviews, forums and blogs to exchange information, practical tips, and stories. Such social interaction has become central to their daily decision-making processes. However, user-authored content is in a free-text format, usually with very scant structured metadata information. Users often face the daunting task of reading a large quantity of text to discover potentially useful information. This thesis addresses the need to automatically leverage information from user-authored text to improve search and to provide personalized recommendations matching user preferences. We first focus on developing accurate text-based recommendations. We the rich information present in user reviews by identifying the review parts pertaining to different product features and the sentiment expressed towards each feature. We derive text-based ratings which serve as alternate indicators of user assessment of the product. We then cluster similar users based on the topics and sentiments in their reviews. Our results show that using text yields better user preference predictions than those from the coarse star ratings. We also make fine-grained predictions of user sentiments towards the individual product features. In the interactive and social forum sites users frequently make connections with other users, enabling them to find the right person to answer their questions. A challenge then, is to score and rank the short snippets of forum posts while taking into account these personal connections.